Velacur ACE outperforms FibroScan CAP for diagnosis of MASLD
Bibliographic record
Abstract
BACKGROUND: As the prevalence of metabolic dysfunction-associated steatotic liver disease increases, it is imperative to have noninvasive alternatives to liver biopsy. Velacur offers a non-invasive, point-of-care ultrasound-based method for the assessment of liver stiffness and attenuation. The aim of this study was to perform a head-to-head comparison of liver stiffness and liver fat determined by Velacur and FibroScan using MRI-based measurements as the reference standard. METHODS: This prospective cross-sectional study included 164 adult participants with well-characterized metabolic dysfunction-associated steatotic liver disease. Patients underwent a research exam including Velacur, FibroScan and contemporaneous magnetic resonance elastography, and magnetic resonance imaging proton density fat fraction (MRI-PDFF) scans. The primary outcome was the presence of advanced fibrosis (>F2) as measured by magnetic resonance elastography and the presence of liver fat (>5%) as measured by MRI-PDFF. RESULTS: The mean age and body mass index were 57±12 years and 30.6±4.8 kg/m2, respectively. The mean liver stiffness on magnetic resonance elastography was 3.22±1.39 kPa and the mean liver fat on MRI-PDFF was 14.2±8%. The liver stiffness assessments by Velacur and FibroScan were similar for the detection of advanced fibrosis (AUC 0.95 vs. 0.97) and were not statistically different (p=0.43). Velacur was significantly better than FibroScan (AUC 0.94 vs. 0.79, p=0.01), for the detection of MRI-PDFF >5% (diagnosis of metabolic dysfunction-associated liver disease). CONCLUSIONS: Velacur was superior to FibroScan for liver fat detection with MRI-PDFF as the reference. Velacur and FibroScan were not statistically different for liver stiffness assessment as defined by magnetic resonance elastography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".